IEEE Geoscience and Remote Sensing Magazine - December 2023 - 41

in remote sensing images, " IEEE Trans. Geosci. Remote Sens.,
vol. 60, pp. 1-11, 2022, doi: 10.1109/TGRS.2021.3091003.
[257] H. Zhang, X. Zhang, G. Meng, C. Guo, and Z. Jiang, " Fewshot
multi-class ship detection in remote sensing images using
attention feature map and multi-relation detector, " Remote
Sens., vol. 14, no. 12, Jun. 2022, Art. no. 2790, doi: 10.3390/
rs14122790.
[258] B. Wang, Z. Wang, X. Sun, H. Wang, and K. Fu, " DMMLNet:
Deep metametric learning for few-shot geographic object
segmentation in remote sensing imagery, " IEEE Trans.
Geosci. Remote Sens., vol. 60, pp. 1-18, 2022, doi: 10.1109/
TGRS.2021.3116672.
[259] J. Li et al., " MM-RCNN: Toward few-shot object detection in
remote sensing images with meta memory, " IEEE Trans. Geosci.
Remote Sens., vol. 60, pp. 1-14, Dec. 2022, doi: 10.1109/
TGRS.2022.3228612.
[260] Z. Zhao, P. Tang, L. Zhao, and Z. Zhang, " Few-shot object detection
of remote sensing images via two-stage fine-tuning, "
IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1-5, 2022, doi:
10.1109/LGRS.2021.3116858.
[261] Y. Zhou, H. Hu, J. Zhao, H. Zhu, R. Yao, and W. Du, " Fewshot
object detection via context-aware aggregation for remote
sensing images, " IEEE Geosci. Remote Sens. Lett., vol. 19, pp.
1-5, 2022, doi: 10.1109/LGRS.2022.3171257.
[262] Y. Wang, C. Xu, C. Liu, and Z. Li, " Context information refinement
for few-shot object detection in remote sensing images, "
Remote Sens., vol. 14, no. 14, Jul. 2022, Art. no. 3255, doi:
10.3390/rs14143255.
[263] Z. Zhou, S. Li, W. Guo, and Y. Gu, " Few-shot aircraft detection
in satellite videos based on feature scale selection pyramid
and proposal contrastive learning, " Remote Sens., vol. 14,
no. 18, Sep. 2022, Art. no. 4581, doi: 10.3390/rs14184581.
[264] S. Chen, J. Zhang, R. Zhan, R. Zhu, and W. Wang, " Few shot
object detection for SAR images via feature enhancement and
dynamic relationship modeling, " Remote Sens., vol. 14, no. 15,
Jul. 2022, Art. no. 3669, doi: 10.3390/rs14153669.
[265] S. Liu, Y. You, H. Su, G. Meng, W. Yang, and F. Liu, " Few-shot
object detection in remote sensing image interpretation: Opportunities
and challenges, " Remote Sens., vol. 14, no. 18, Sep.
2022, Art. no. 4435, doi: 10.3390/rs14184435.
[266] X. Huang, B. He, M. Tong, D. Wang, and C. He, " Few-shot
object detection on remote sensing images via shared attention
module and balanced fine-tuning strategy, " Remote
Sens., vol. 13, no. 19, Sep. 2021, Art. no. 3816, doi: 10.3390/
rs13193816.
[267] Z. Xiao, J. Qi, W. Xue, and P. Zhong, " Few-shot object detection
with self-adaptive
attention network for remote
sensing images, " IEEE J. Sel. Topics Appl. Earth Observ. Remote
Sens., vol. 14, pp. 4854-4865, May 2021, doi: 10.1109/
JSTARS.2021.3078177.
[268] S. Wolf, J. Meier, L. Sommer, and J. Beyerer, " Double head predictor
based few-shot object detection for aerial imagery, " in
Proc. IEEE Int. Conf. Comput. Vision Workshops, 2021, pp. 721-
731, doi: 10.1109/ICCVW54120.2021.00086.
[269] T. Zhang, X. Zhang, P. Zhu, X. Jia, X. Tang, and L. Jiao, " Generalized
few-shot object detection in remote sensing images, "
DECEMBER 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
ISPRS J. Photogrammetry Remote Sens., vol. 195, pp. 353-364,
Jan. 2023, doi: 10.1016/j.isprsjprs.2022.12.004.
[270] G. Heitz and D. Koller, " Learning spatial context: Using stuff
to find things, " in Proc. Eur. Conf. Comput. Vision, Berlin, Heidelberg:
Springer, 2008, vol. 5302, pp. 30-43.
[271] C. Benedek, X. Descombes, and J. Zerubia, " Building development
monitoring in multitemporal remotely sensed image
pairs with stochastic birth-death dynamics, " IEEE Trans. Pattern
Anal. Mach. Intell., vol. 34, no. 1, pp. 33-50, Jan. 2012, doi:
10.1109/TPAMI.2011.94.
[272] S. Razakarivony and F. Jurie, " Vehicle detection in aerial imagery:
A small target detection benchmark, " J. Vis. Commun.
Image Representation, vol. 34, pp. 187-203, Jan. 2016, doi:
10.1016/j.jvcir.2015.11.002.
[273] K. Liu and G. Máttyus, " Fast multiclass vehicle detection on
aerial images, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 9, pp.
1938-1942, Sep. 2015, doi: 10.1109/LGRS.2015.2439517.
[274] H. Zhu, X. Chen, W. Dai, K. Fu, Q. Ye, and J. Jiao, " Orientation
robust object detection in aerial images using deep convolutional
neural network, " in Proc. IEEE Int. Conf. Image Process.,
2015, pp. 3735-3739, doi: 10.1109/ICIP.2015.7351502.
[275] T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye,
" A large contextual dataset for classification, detection and
counting of cars with deep learning, " in Proc. Eur. Conf. Comput.
Vision, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds.
Cham, Switzerland: Springer, 2016, vol. 9907, pp. 785-800.
[276] Z. Liu, H. Wang, L. Weng, and Y. Yang, " Ship rotated bounding
box space for ship extraction from high-resolution optical
satellite images with complex backgrounds, " IEEE Geosci. Remote
Sens. Lett., vol. 13, no. 8, pp. 1074-1078, Aug. 2016, doi:
10.1109/LGRS.2016.2565705.
[277] J. Li, C. Qu, and J. Shao, " Ship detection in SAR images based
on an improved faster R-CNN, " in Proc. SAR Big Data Era, Models,
Methods Appl. (BIGSARDATA), 2017, pp. 1-6, doi: 10.1109/
BIGSARDATA.2017.8124934.
[278] Z. Zou and Z. Shi, " Random access memories: A new paradigm
for target detection in high resolution aerial remote
sensing images, " IEEE Trans. Image Process., vol. 27, no. 3, pp.
1100-1111, Mar. 2018, doi: 10.1109/TIP.2017.2773199.
[279] X. Sun, Z. Wang, Y. Sun, W. Diao, Y. Zhang, and K. Fu, " Airsarship-1.0:
High-resolution SAR ship detection dataset, " J.
Radars, vol. 8, no. 6, pp. 852-863, Dec. 2019, doi: 10.12000/
JR19097.
[280] W. Yu et al., " Mar20: A benchmark for military aircraft recognition
in remote sensing images, " in Proc. Nat. Remote Sens.
Bull., 2022, pp. 1-11, doi: 10.11834/jrs.20222139.
[281] K. Chen, M. Wu, J. Liu, and C. Zhang, " FGSD: A dataset
for fine-grained ship detection in high resolution satellite
images, " 2020. [Online]. Available: https://arxiv.org/
abs/2003.06832
[282] Y. Han, X. Yang, T. Pu, and Z. Peng, " Fine-grained recognition
for oriented ship against complex scenes in optical remote
sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 60, pp.
1-18, 2022, doi: 10.1109/TGRS.2021.3123666.
[283] J. Wang, W. Yang, H. Guo, R. Zhang, and G. Xia, " Tiny object
detection in aerial images, " in Proc. IEEE Int. Conf. Pattern
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IEEE Geoscience and Remote Sensing Magazine - December 2023

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2023
